thelamapi/next-4b

🚀 Next 4B (s330)

Türkiye’s First Vision-Language Model — Efficient, Multimodal, and Reasoning-Focused

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📖 Overview

Next 4B is a 4-billion parameter multimodal Vision-Language Model (VLM) based on Gemma 3, fine-tuned to handle both text and images efficiently. It is Türkiye’s first open-source vision-language model, designed for:

• Understanding and generating text and image descriptions.
• Efficient reasoning and context-aware multimodal outputs.
• Turkish support with multilingual capabilities.
• Low-resource deployment using 8-bit quantization for consumer-grade GPUs.

This model is ideal for researchers, developers, and organizations who need a high-performance multimodal AI capable of visual understanding, reasoning, and creative generation.

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Our Next 1B and Next 4B models are leading to all of the tiny models in benchmarks.

Model	MMLU (5-shot) %	MMLU-Pro %	GSM8K %	MATH %
Next 4B preview	84.6	66.9	82.7	70.5
Next 1B	87.3	69.2	90.5	70.1
Qwen 3 0.6B	52.81	37.6	60.7	20.5
Llama 3.2 1B	49.3	44.4	11.9	30.6

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Also, our Next 14b model is leading to state-of-the-art models in some of the Benchmarks.

Model	MMLU (5-shot) %	MMLU-Pro %	GSM8K %	MATH %
Next 14B (Thinking)	94.6	93.2	98.8	92.7
Next 12B	92.7	84.4	95.3	87.2
GPT-5	92.5	87.0	98.4	96.0
Claude Opus 4.1 (Thinking)	~92.0	87.8	84.7	95.4

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🚀 Installation & Usage

Use with vision:

from transformers import AutoTokenizer, AutoModelForCausalLM, AutoProcessor
from PIL import Image
import torch

model_id = "Lamapi/next-4b"

model = AutoModelForCausalLM.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id) # For vision.
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Read image
image = Image.open("image.jpg")

# Create a message in chat format
messages = [
  {"role": "system","content": [{"type": "text", "text": "You are Next-X1, a smart and concise AI assistant trained by Lamapi. Always respond in the user's language. Proudly made in Turkey."}]},

  {
      "role": "user","content": [{"type": "image", "image": image},
      {"type": "text", "text": "Who is in this image?"}
    ]
  }
]

# Prepare input with Tokenizer
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=prompt, images=[image], return_tensors="pt")

# Output from the model
output = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Who is in this image?

The image shows Mustafa Kemal Atatürk, the founder and first President of the Republic of Turkey.

Use without vision:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Lamapi/next-4b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Chat message
messages = [
    {"role": "system", "content": "You are Next-X1, a smart and concise AI assistant trained by Lamapi. Always respond in the user's language. Proudly made in Turkey."},
    {"role": "user", "content": "Hello, how are you?"}
]

# Prepare input with Tokenizer
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt")

# Output from the model
output = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Hello, how are you?

I'm fine, thank you. How are you?

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🎯 Goals

1. Multimodal Intelligence: Understand and reason over images and text.
2. Efficiency: Run on modest GPUs using 8-bit quantization.
3. Accessibility: Open-source availability for research and applications.
4. Cultural Relevance: Optimized for Turkish language and context while remaining multilingual.

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✨ Key Features

Feature	Description
🔋 Efficient Architecture	Optimized for low VRAM; supports 8-bit quantization for consumer GPUs.
🖼️ Vision-Language Capable	Understands images, captions them, and performs visual reasoning tasks.
🇹🇷 Multilingual & Turkish-Ready	Handles complex Turkish text with high accuracy.
🧠 Advanced Reasoning	Supports logical and analytical reasoning for both text and images.
📊 Consistent & Reliable Outputs	Reproducible responses across multiple runs.
🌍 Open Source	Transparent, community-driven, and research-friendly.

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📐 Model Specifications

Specification	Details
Base Model	Gemma 3
Parameter Count	4 Billion
Architecture	Transformer, causal LLM + Vision Encoder
Fine-Tuning Method	Instruction & multimodal fine-tuning (SFT) on Turkish and multilingual datasets
Optimizations	Q8_0, F16, F32 quantizations for low VRAM and high VRAM usage
Modalities	Text & Image
Use Cases	Image captioning, multimodal QA, text generation, reasoning, creative storytelling

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📄 License

This project is licensed under the MIT License — free to use, modify, and distribute. Attribution is appreciated.

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📞 Contact & Support

• 📧 Email: lamapicontact@gmail.com
• 🤗 HuggingFace: Lamapi

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Next 4B — Türkiye’s first vision-language AI, combining multimodal understanding, reasoning, and efficiency.